English: C1 – Advanced / Fluent Professional Proficiency
Advanced/Fluent professional proficiency, suitable for technical discussions, meetings, documentation, and collaboration with international teams. Weak English communication may result in rejection because the role requires frequent cross-functional teamwork.
About the job
Location: Remote
Position Overview
Senior Data Engineer is a hands-on technical role responsible for designing, building, and maintaining the data pipelines, warehouse structures, and cloud infrastructure that power analytics, reporting, and machine learning across our clients’ programs and operations. This role supports the full data engineering lifecycle—from ingestion and transformation through orchestration, optimization, and production reliability. The ideal candidate is a strong engineer who communicates clearly with non-technical audiences and is motivated by the opportunity to build scalable data systems that support diverse industries including healthcare, retail, ecommerce, logistics, and more.
Duties/Responsibilitie
Design, build, and maintain data pipelines that ingest, transform, and deliver high‑quality data for analytics, reporting, and machine learning use cases across multiple business domains.
Develop and optimize data models and warehouse structures (dimensional, relational, and lakehouse) to support scalable, performant querying and downstream consumption.
Ensure data reliability and quality through validation frameworks, schema enforcement, reconciliation processes, and automated monitoring for anomalies or pipeline failures.
Implement robust orchestration and workflow automation using tools such as Airflow, Dagster, or Prefect, ensuring pipelines run efficiently and recover gracefully from errors.
Monitor production pipelines for drift, latency, and performance degradation, leading remediation efforts and continuous improvement initiatives.
SQL and Python fluent: Write advanced SQL for complex transformations and maintain clean, modular Python code for ETL/ELT, automation, and distributed data processing.
Cloud-native engineering: Build and maintain data systems using cloud services (AWS, Azure, or GCP), including storage, compute, orchestration, and serverless components.
A systems builder: Create scalable, maintainable data infrastructure—streaming pipelines, batch jobs, curated datasets, semantic layers—that reliably support analytics and ML teams.
A driver of adoption: Partner with analysts, data scientists, and business stakeholders to ensure the data products you build are usable, trusted, and integrated into real workflows.
Required Skills & Qualifications
Strong Python and SQL skills for ETL/ELT, automation, data processing, and complex transformations; experience with pandas, PySpark, and/or SQLAlchemy.
Hands-on experience with Snowflake, Redshift, BigQuery, or similar platforms, including data modeling, ELT, and performance optimization.
Experience with Spark, Hadoop, Kafka, Kinesis, or other batch and streaming technologies.
Proficiency with AWS, Azure, or GCP data services.
Experience with Airflow, Dagster, Prefect, or similar orchestration tools.
Knowledge of data quality, governance, security, PII, and GDPR/CCPA compliance.
Experience with CI/CD, Terraform/IaC, Docker, and version-controlled workflows.
Knowledge of lakehouse architectures and technologies such as Delta Lake, Iceberg, or Hudi.
Familiarity with MLOps, feature pipelines, semantic layers, and BI tools such as Power BI, Tableau, or Looker.
Our Company helps small to medsized companies promptly derive actionable insights out of disintegrated business data with AI software. Since 2015, It helps its clients make smarter decisions by implementing intuitive end-to-end BI solutions and rendering AI support services.